code: def test_trainer_hyperparameter_tuning_does_not_log_sklearn_model(
      transformers_hyperparameter_trainer,
  ):
      mlflow.autolog()
  
      exp = mlflow.set_experiment(experiment_name="hyperparam_trainer")
  
      transformers_hyperparameter_trainer.train()
  
      last_run = mlflow.last_active_run()
      assert last_run.data.metrics["epoch"] == 3.0
      assert last_run.data.params["model_type"] == "distilbert"
  
      pipe = pipeline(
          task="text-classification",
          model=transformers_hyperparameter_trainer.model,
          tokenizer=DistilBertTokenizerFast.from_pretrained(
              "distilbert-base-uncased"
          ),
      )
      assert (
          len(pipe("This is wonderful!")[0]["label"]) > 5
      )  # checking for 'LABEL_0' or 'LABEL_1'
  
      client = mlflow.MlflowClient()
      runs = client.search_runs([exp.experiment_id])
  
      assert len(runs) == 1
  
imports: import random
  import numpy
  import optuna
  import pytest
  import sklearn
  import sklearn.cluster
  import torch
  from datasets import load_dataset
  from sentence_transformers.losses import CosineSimilarityLoss
  from setfit import SetFitModel, sample_dataset
  from setfit import Trainer
  from setfit import TrainingArguments
  from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast, Trainer, TrainingArguments, pipeline
  import mlflow
label_desc: Use image node_id 14 for calling a global function with example usage: test_trainer_hyperparameter_tuning_does_not_log_sklearn_model(transformers_hyperparameter_trainer) without return types